{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fe4fe15e",
   "metadata": {},
   "source": [
    "**<font size=4 color=#BB3D00 face=微软雅黑>使用互相关对齐信号</font>**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd1e634e",
   "metadata": {},
   "source": [
    "<font size=3  face=微软雅黑>**※Matlab案例**</font> "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "195d24d1",
   "metadata": {},
   "source": [
    "网址：https://ww2.mathworks.cn/help/signal/ug/align-signals-using-cross-correlation.html   \n",
    "描述：本案例由4个示例构成： \n",
    "- <font color=DarkOrChid>示例1：信号有不同到达时间</font>\n",
    "- <font color=DarkOrChid>示例2：计算三对信号之间的互相关性</font>\n",
    "- <font color=DarkOrChid>示例3：绘制互相关图</font>\n",
    "- <font color=DarkOrChid>示例4：对齐信号</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53889ca0",
   "metadata": {},
   "source": [
    "<font size=3 face=微软雅黑>**※Python案例**</font> "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16f8cace",
   "metadata": {},
   "source": [
    "针对以上案例，采用Python语言实现。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d0d316d",
   "metadata": {},
   "source": [
    "- <font color=DarkOrChid>示例1：信号有不同到达时间</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2531c839",
   "metadata": {},
   "source": [
    "许多测量涉及多个传感器异步采集的数据。如果您要集成信号并以关联式研究它们，您必须同步它们。\n",
    "\n",
    "例如，假设有一辆汽车经过一座桥。它产生的振动由位于不同位置的三个相同传感器进行测量。信号有不同到达时间。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e6ab7c41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np  \n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "x = np.arange(0,25) \n",
    "y1=[0,0,0,0,0,0,0,2,3,-1,3,7,9,-8,6,4,-6,-7,8,0,0,0,0,0,0]\n",
    "y2=[0,0,2,3,-1,3,7,9,-8,6,4,-6,-7,8,0,0,0,0,0,0,0,0,0,0,0]\n",
    "y3=[0,0,0,0,0,0,0,0,0,0,0,2,3,-1,3,7,9,-8,6,4,-6,-7,8,0,0]\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(311)\n",
    "ax1.plot(x,y1) \n",
    "ax1 = fig.add_subplot(312)\n",
    "ax1.plot(x,y2) \n",
    "ax1 = fig.add_subplot(313)\n",
    "ax1.plot(x,y3) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b896f040",
   "metadata": {},
   "source": [
    "- <font color=DarkOrChid>示例2：计算三对信号之间的互相关性</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45fcf0f6",
   "metadata": {},
   "source": [
    "计算三对信号之间的互相关性。将它们归一化，使其最大值为 1。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ffb05e1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "cq12=np.correlate(y1,y2,'full')\n",
    "cq13=np.correlate(y1,y3,'full')\n",
    "cq23=np.correlate(y2,y3,'full')\n",
    "c12=cq12/max(cq12)\n",
    "c13=cq13/max(cq13)\n",
    "c23=cq23/max(cq23)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2e6d8af",
   "metadata": {},
   "source": [
    "互相关性最大值的位置指示领先或滞后时间。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2e040b72",
   "metadata": {},
   "outputs": [],
   "source": [
    "t12=np.argmax(c12)-len(y1)+1\n",
    "t13=np.argmax(c13)-len(y1)+1\n",
    "t23=np.argmax(c23)-len(y2)+1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c876f4e",
   "metadata": {},
   "source": [
    "- <font color=DarkOrChid>示例3：绘制互相关图</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02272ca3",
   "metadata": {},
   "source": [
    "绘制互相关图。在每个绘图中显示最大值的位置。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "490dd4ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x1 = np.arange(-len(y1)+1,len(y2)) \n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(311)\n",
    "ax1.plot(x1,c12) \n",
    "plt.scatter(t12,1)\n",
    "plt.text(t12+1,1-0.3, 'lag:%i'%t12,fontsize=15)\n",
    "ax1 = fig.add_subplot(312)\n",
    "ax1.plot(x1,c13) \n",
    "plt.scatter(t13,1)\n",
    "plt.text(t13+1,1-0.3, 'lag:%i'%t13,fontsize=15)\n",
    "ax1 = fig.add_subplot(313)\n",
    "ax1.plot(x1,c23) \n",
    "plt.scatter(t23,1)\n",
    "plt.text(t23+1,1-0.3, 'lag:%i'%t23,fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2ede413",
   "metadata": {},
   "source": [
    "- <font color=DarkOrChid>示例4：对齐信号</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a3d6492",
   "metadata": {},
   "source": [
    "通过截断具有较长延迟的向量来对齐信号。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0fc4e23c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y1 = y1[t12:]\n",
    "y1+=([0]*t12)\n",
    "y3 = y3[-t23:]\n",
    "y3+=[0]*(-t23)\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(311)\n",
    "ax1.plot(x,y1) \n",
    "ax1 = fig.add_subplot(312)\n",
    "ax1.plot(x,y2) \n",
    "ax1 = fig.add_subplot(313)\n",
    "ax1.plot(x,y3) \n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
